Micro Focus Enterprise Server 6.0 Documentation
Johanne
This pattern introduces a scalable architecture for mainframe applications using Micro Focus Enterprise Server in Scale-Out Performance and Availability Cluster (PAC) and an Amazon Elastic Compute Cloud (Amazon EC2) Auto Scaling group on Amazon Web Services (AWS). The solution is fully automated with AWS Systems Manager and Amazon EC2 Auto Scaling lifecycle hooks. By using this pattern, you can set up your mainframe online and batch applications to achieve high resiliency by automatically scaling in and out based on your capacity demands.
An understanding of the overall concept of mainframe application DevOps with continuous integration (CI). For an AWS Prescriptive Guidance pattern that was developed by AWS and Micro Focus, see Mainframe modernization: DevOps on AWS with Micro Focus.
In the conventional mainframe environment, you must provision hardware to host your applications and corporate data. To cater for and meet the spikes in seasonal, monthly, quarterly, or even unprecedented or unexpected demands, mainframe users must scale out by purchasing additional storage and compute capacity. Increasing the number of storage and compute capacity resources improves overall performance, but the scaling is not linear.
This is not the case when you start adopting an on-demand consumption model on AWS by using Amazon EC2 Auto Scaling and Micro Focus Enterprise Servers. The following sections explain detail how to build a fully automated, scalable mainframe application architecture using Micro Focus Enterprise Server Scale-Out Performance and Availability Cluster (PAC) with an Amazon EC2 Auto Scaling group.
First, it is important to understand the basic concepts of Micro Focus Enterprise Server. This environment provides a mainframe-compatible, x86 deployment environment for applications that have traditionally run on the IBM mainframe. It delivers both online and batch runs and a transaction environment that supports the following:
Micro Focus Enterprise Server enables mainframe applications to run with minimal changes. Existing mainframe workloads can be moved to x86 platforms and modernized to take advantage of AWS Cloud native extensions for rapid expansion to new markets or geographies.
The AWS Prescriptive Guidance pattern Mainframe modernization: DevOps on AWS with Micro Focus introduced the architecture to accelerate the development and testing of mainframe applications on AWS using Micro Focus Enterprise Developer and Enterprise Test Server with AWS CodePipeline and AWS CodeBuild. This pattern focuses on the deployment of mainframe applications to the AWS production environment to achieve high availability and resiliency.
In a mainframe production environment, you might have set up IBM Parallel Sysplex in the mainframe to achieve high performance and high availability. To create a scale-out architecture similar to Sysplex, Micro Focus introduced the Performance and Availability Cluster (PAC) to Enterprise Server. PACs support mainframe application deployment onto multiple Enterprise Server regions managed as a single image and scaled out in Amazon EC2 instances. PACs also support predictable application performance and system throughput on demand.
In a PAC, multiple Enterprise Server instances work together as a single logical entity. Failure of one Enterprise Server instance, therefore, will not interrupt business continuity because capacity is shared with other regions while new instances are automatically started using industry standard functionality such as an Amazon EC2 Auto Scaling group. This removes single points of failure, improving resilience to hardware, network, and application issues. Scaled-out Enterprise Server instances can be operated and managed by using the Enterprise Server Common Web Administration (ESCWA) APIs, simplifying the operational maintenance and serviceability of Enterprise Servers.
Note: Micro Focus recommends that the Performance and Availability Cluster (PAC) should consist of at least three Enterprise Server regions so that availability is not compromised in the event an Enterprise Server region fails or requires maintenance.
PAC configuration requires a supported relational database management service (RDBMS) to manage the region database, a cross-region database, and optional data store databases. A data store database should be used to managed Virtual Storage Access Method (VSAM) files using the Micro Focus Database File Handler support to improve availability and scalability. Supported RDBMSs include the following:
Set up an automatic scaling group deployed with Enterprise Server Common Web Administration (ESCWA). ESCWA provides cluster management APIs. The ESCWA servers act as a control plane to add or remove Enterprise Servers and start or stop Enterprise Server regions in the PAC during the Enterprise Server instance automatic scaling events. Because the ESCWA instance is used only for the PAC management, its traffic pattern is predictable, and its automatic scaling desired capacity requirement can be set to 1.
Set up an ElastiCache Redis primary instance and at least one replica to host user data and act as a scale-out repository (SOR) for the Enterprise Server instances. You can configure one or more scale-out repository to store specific types of user data. Enterprise Server uses a Redis NoSQL database as an SOR, a requirement to maintain PAC integrity.
To automate the cluster management tasks during automatic scaling events, you can use Systems Manager Automation runbooks and Amazon EC2 Auto Scaling with Amazon EventBridge. The architecture of these automations is shown in the following diagram.
Create an Amazon EC2 Windows Server instance and install the Micro Focus Enterprise Server binary in the EC2 instance. Create an Amazon Machine Image (AMI) of the EC2 instance. For more information, see the Enterprise Server installation documentation.
Use the example code snippets in the Additional information section to make a CloudFormation template that will create a Systems Manager Automation runbook for automating PAC creation, Enterprise Server scale in, and Enterprise Server scale out.
Use the AWS example code snippet to make a CloudFormation template that will create an automatic scaling group. This template will reuse the same AMI that was created for the Micro Focus Enterprise Server ESCWA instance.
At the start of a PAC cluster, Enterprise Server requires ESCWA to invoke APIs to create a PAC configuration. This starts and adds Enterprise Server regions into the PAC. To create or recreate a PAC, use the following steps:
Create an Amazon EC2 launch template that downloads or creates the Windows PowerShell script as part of the bootstrap process. For example, you can use EC2 user data to download the script from an Amazon Simple Storage Service (Amazon S3) bucket.
When an Enterprise Server instance is scaled out, its Enterprise Server region must be added to the PAC. The following steps explain how to invoke ESCWA APIs and add the Enterprise Server region into the PAC.
Create an Amazon EC2 launch template for Enterprise Server instance that provisions an Enterprise Server Region during its bootstrap. For example, you can use the Micro Focus Enterprise Server command mfds to import a region configuration. For further details and options available for this command, see the Enterprise Server Reference.
Similar to scaling out, when an Enterprise Server instance is scaled in, the event EC2 Instance-terminate Lifecycle Action is initiated, and the following process and API calls are needed to remove a Micro Focus Enterprise Server instance from the PAC.
The process of setting up a scaling policy for Enterprise Server instances requires an understanding of the application behavior. In most cases, you can set up target tracking scaling policies. For example, you can use the average CPU utilization as the Amazon CloudWatch metric to set for the automatic scaling policy. For more information, see Target tracking scaling policies for Amazon EC2 Auto Scaling. For applications that have regular traffic patterns, consider using a predictive scaling policy. For more information, see Predictive scaling for Amazon EC2 Auto Scaling.
Certain commercial entities, equipment, products, or materials may be identified by name or company logo or other insignia in order to acknowledge theirparticipation in this collaboration or to describe an experimental procedure or concept adequately. Such identification is not intended to imply special statusor relationship with NIST or recommendation or endorsement by NIST or NCCoE; neither is it intended to imply that the entities, equipment, products, ormaterials are necessarily the best available for the purpose.
As a private-public partnership, we are always seeking feedback on our practice guides. We are particularly interested in seeing how businesses apply NCCoEreference designs in the real world. If you have implemented the reference design, or have questions about applying it in your environment, please email us atds-...@nist.gov.
NIST Cybersecurity Practice Guides (Special Publication 1800 series) target specific cybersecurity challenges in the public and private sectors. They arepractical, user-friendly guides that facilitate the adoption of standards-based approaches to cybersecurity. They show members of the information securitycommunity how to implement example solutions that help them align with relevant standards and best practices, and provide users with the materials lists,configuration files, and other information they need to implement a similar approach.
The documents in this series describe example implementations of cybersecurity practices that businesses and other organizations may voluntarily adopt. Thesedocuments do not describe regulations or mandatory practices, nor do they carry statutory authority.
The Technology Partners/Collaborators who participated in this build submitted their capabilities in response to a notice in the Federal Register.Respondents with relevant capabilities or product components were invited to sign a Cooperative Research and Development Agreement (CRADA) with NIST, allowingthem to participate in a consortium to build this example solution. We worked with:
c80f0f1006